From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Models
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Wen, Xumeng, Zhang, Han, Zheng, Shun, Xu, Wei, Bian, Jiang |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models
par: Wen, Xumeng, et autres
Publié: (2025)
par: Wen, Xumeng, et autres
Publié: (2025)
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains
par: Zhang, Jiawen, et autres
Publié: (2025)
par: Zhang, Jiawen, et autres
Publié: (2025)
ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons
par: Zhang, Jiawen, et autres
Publié: (2023)
par: Zhang, Jiawen, et autres
Publié: (2023)
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
par: Zhang, Jiawen, et autres
Publié: (2024)
par: Zhang, Jiawen, et autres
Publié: (2024)
A Survey on Self-Supervised Learning for Non-Sequential Tabular Data
par: Wang, Wei-Yao, et autres
Publié: (2024)
par: Wang, Wei-Yao, et autres
Publié: (2024)
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining
par: Lin, Chenguo, et autres
Publié: (2023)
par: Lin, Chenguo, et autres
Publié: (2023)
FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning
par: Yang, Zhihan, et autres
Publié: (2026)
par: Yang, Zhihan, et autres
Publié: (2026)
Learning to Select In-Context Demonstration Preferred by Large Language Model
par: Zhang, Zheng, et autres
Publié: (2025)
par: Zhang, Zheng, et autres
Publié: (2025)
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
par: Han, Sungwon, et autres
Publié: (2024)
par: Han, Sungwon, et autres
Publié: (2024)
Generating Realistic Tabular Data with Large Language Models
par: Nguyen, Dang, et autres
Publié: (2024)
par: Nguyen, Dang, et autres
Publié: (2024)
Large Language Model as a Universal Clinical Multi-task Decoder
par: Wu, Yujiang, et autres
Publié: (2024)
par: Wu, Yujiang, et autres
Publié: (2024)
Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later
par: Ye, Han-Jia, et autres
Publié: (2024)
par: Ye, Han-Jia, et autres
Publié: (2024)
Mambular: A Sequential Model for Tabular Deep Learning
par: Thielmann, Anton Frederik, et autres
Publié: (2024)
par: Thielmann, Anton Frederik, et autres
Publié: (2024)
The Illusion of Generalization in Tabular Language Models
par: Gorla, Aditya, et autres
Publié: (2026)
par: Gorla, Aditya, et autres
Publié: (2026)
How Well Does Your Tabular Generator Learn the Structure of Tabular Data?
par: Jiang, Xiangjian, et autres
Publié: (2025)
par: Jiang, Xiangjian, et autres
Publié: (2025)
PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
par: Ye, Hangting, et autres
Publié: (2024)
par: Ye, Hangting, et autres
Publié: (2024)
Tabular Foundation Model for Generative Modelling
par: Jiang, Xiangjian, et autres
Publié: (2026)
par: Jiang, Xiangjian, et autres
Publié: (2026)
ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
par: Wei, Jack Yi, et autres
Publié: (2026)
par: Wei, Jack Yi, et autres
Publié: (2026)
Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking
par: Herurkar, Dayananda, et autres
Publié: (2025)
par: Herurkar, Dayananda, et autres
Publié: (2025)
Improving Deep Tabular Learning
par: Sarafian, Sivan, et autres
Publié: (2025)
par: Sarafian, Sivan, et autres
Publié: (2025)
MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data
par: Ling, Yaobin, et autres
Publié: (2024)
par: Ling, Yaobin, et autres
Publié: (2024)
A Novel Data-Dependent Learning Paradigm for Large Hypothesis Classes
par: Pour, Alireza F., et autres
Publié: (2025)
par: Pour, Alireza F., et autres
Publié: (2025)
A Closer Look at Deep Learning Methods on Tabular Datasets
par: Ye, Han-Jia, et autres
Publié: (2024)
par: Ye, Han-Jia, et autres
Publié: (2024)
Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalanced Regression
par: Wang, Yung-Chien, et autres
Publié: (2025)
par: Wang, Yung-Chien, et autres
Publié: (2025)
BatteryML:An Open-source platform for Machine Learning on Battery Degradation
par: Zhang, Han, et autres
Publié: (2023)
par: Zhang, Han, et autres
Publié: (2023)
Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning
par: Han, Xinyan, et autres
Publié: (2026)
par: Han, Xinyan, et autres
Publié: (2026)
Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning
par: Ono, Kyoka, et autres
Publié: (2024)
par: Ono, Kyoka, et autres
Publié: (2024)
ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction
par: Zhang, Yanlin, et autres
Publié: (2024)
par: Zhang, Yanlin, et autres
Publié: (2024)
Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
par: Li, Dongyuan, et autres
Publié: (2026)
par: Li, Dongyuan, et autres
Publié: (2026)
LLM Embeddings for Deep Learning on Tabular Data
par: Koloski, Boshko, et autres
Publié: (2025)
par: Koloski, Boshko, et autres
Publié: (2025)
A Novel Paradigm in Solving Multiscale Problems
par: Wang, Jing, et autres
Publié: (2024)
par: Wang, Jing, et autres
Publié: (2024)
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations
par: Hu, Yaojie, et autres
Publié: (2024)
par: Hu, Yaojie, et autres
Publié: (2024)
Statistically Accurate and Robust Generative Prediction of Rock Discontinuities with A Tabular Foundation Model
par: Meng, Han, et autres
Publié: (2025)
par: Meng, Han, et autres
Publié: (2025)
Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness
par: Guo, Kai, et autres
Publié: (2024)
par: Guo, Kai, et autres
Publié: (2024)
UniPredict: Large Language Models are Universal Tabular Classifiers
par: Wang, Ruiyu, et autres
Publié: (2023)
par: Wang, Ruiyu, et autres
Publié: (2023)
Deep Tabular Representation Corrector
par: Ye, Hangting, et autres
Publié: (2026)
par: Ye, Hangting, et autres
Publié: (2026)
A Note on Statistically Accurate Tabular Data Generation Using Large Language Models
par: Sidorenko, Andrey
Publié: (2025)
par: Sidorenko, Andrey
Publié: (2025)
A Survey on Deep Tabular Learning
par: Somvanshi, Shriyank, et autres
Publié: (2024)
par: Somvanshi, Shriyank, et autres
Publié: (2024)
Transfer Learning of Tabular Data by Finetuning Large Language Models
par: Rabbani, Shourav B., et autres
Publié: (2025)
par: Rabbani, Shourav B., et autres
Publié: (2025)
From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning
par: Kumar, Manish, et autres
Publié: (2026)
par: Kumar, Manish, et autres
Publié: (2026)
Documents similaires
-
Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models
par: Wen, Xumeng, et autres
Publié: (2025) -
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains
par: Zhang, Jiawen, et autres
Publié: (2025) -
ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons
par: Zhang, Jiawen, et autres
Publié: (2023) -
ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
par: Zhang, Jiawen, et autres
Publié: (2024) -
A Survey on Self-Supervised Learning for Non-Sequential Tabular Data
par: Wang, Wei-Yao, et autres
Publié: (2024)